Interested in this AI/ML Engineer role at Axos Bank?
Apply Now →Skills & Technologies
About This Role
Axos BankTarget Range:
$30.00 /Hr. - $30.00 /Hr. Actual starting pay will vary based on factors including, but not limited to, geographic location, experience, skills, specialty, and education.
Eligible for an Annual Discretionary Cash Bonus Target: 10% Eligible for an Annual Discretionary Restricted Stock Units Bonus Target: 10%These discretionary target bonuses may be awarded semi-annually based upon your achievement of performance goals and targets.
About This Job
This is an exciting opportunity to join a unique and immersive program as a first step in your career in technology. This full-time rotational program is geared toward providing multi-software platform exposure that focuses on the expansion of knowledge and real-life application.
As a Junior AI Engineer, you'll be embedded in the Consumer IT AI Team, working alongside experienced engineers on real production systems from day one. You'll receive structured mentorship, exposure to the full AI engineering stack, and a clear development path toward Intermediate and Senior roles. This is an onsite position at our HQ in San Diego, CA. Hybrid or remote options will not be available for this position.
Responsibilities:
- Support development and testing of AI agents under close mentorship from senior engineers
- Learn and apply prompt engineering fundamentals to assigned domain tasks
- Write and maintain integration code connecting AI systems to internal platforms (GitHub, Azure DevOps, Linear, Confluence)
- Assist in building and validating Model Context Protocol (MCP) server components
- Help maintain and document AI skills and plugins in the team's shared plugin library
- Review and log AI model outputs to support quality tracking and improvement cycles
- Write unit tests and participate in code reviews as part of the team's CI/CD workflow
- Participate in sprint ceremonies, standups, and planning sessions to build SDLC fluency
- Contribute to internal documentation efforts including Confluence pages and runbooks
- Bring fresh perspectives and curiosity — ask questions, share ideas, and grow fast
Qualifications:
- Bachelor's Degree in Computer Science, Software Engineering, Information Technology, Mathematics, Data Science, or a related field
- Coursework or personal projects demonstrating programming experience in Python, JavaScript, or a similar language
- Familiarity with version control concepts (Git) and basic software development workflows
- Academic or project exposure to APIs, data structures, or software testing practices
- Curiosity & Growth Mindset: Eager to learn quickly, take on new challenges, and apply feedback in a fast-moving environment.
- Collaborative Spirit: Works well in a team setting, asks for help when needed, and contributes positively to team culture.
- Attention to Detail: Careful and methodical in testing, documentation, and reviewing AI outputs for accuracy.
- Communication: Able to articulate ideas clearly in writing and in team settings; listens actively and asks clarifying questions.
- Ethics & Integrity: Understands the importance of responsible AI practices and approaches work with honesty and professionalism.
- Problem Solving: Approaches challenges with a structured mindset; breaks down problems and seeks to understand root causes.
Axos Employee Benefits May Include:
- Medical, Dental, Vision, and Life Insurance
- Paid Sick Leave, 3 weeks’ Vacation, and Holidays (about 11 a year)
- HSA or FSA account and other voluntary benefits
- 401(k) Retirement Saving Plan with Employer Match Program and 529 Savings Plan
- Employee Mortgage Loan Program and free access to an Axos Bank Account with Self-Directed Trading
About Axos
Born digital-first, Axos delivers financial tools and services that allow individuals, small businesses, and companies to access and manage their money how, when, and where they want. We’re a diverse team of dynamic, insightful, and independent innovators who are excited to provide technology-driven solutions that offer unbeatable value to our customers.
Axos Financial is our holding company and is publicly traded on the New York Stock Exchange under the symbol "AX" (NYSE: AX).
Learn more about working at Axos
Pre-Employment Background Check and Drug Test:
All offers are contingent upon the candidate successfully passing a credit check, criminal background check, and pre-employment drug screening, which includes screening for marijuana. Axos Bank is a federally regulated banking institution. At the federal level, marijuana is an illegal schedule 1 drug; therefore, we will not employ any person who tests positive for marijuana, regardless of state legalization.
Equal Employment Opportunity:
Axos is an Equal Opportunity employer. We are committed to providing equal employment opportunities to all employees and applicants without regard to race, religious creed, color, sex (including pregnancy, breast feeding and related medical conditions), gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship status, military and veteran status, marital status, age, protected medical condition, genetic information, physical disability, mental disability, or any other protected status in accordance with all applicable federal, state, and local laws.
Job Functions and Work Environment:
While performing the duties of this position, the employee is required to sit for extended periods of time. Manual dexterity and coordination are required while operating standard office equipment such as computer keyboard and mouse, calculator, telephone, copiers, etc.
The work environment characteristics described here are representative of those an employee may encounter while performing the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position.
E-Verify and Right to Work Notices
Axos participates in the U.S. Department of Homeland Security E-Verify program in all facilities located in the United States. The E-Verify program is an internet-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services.
IER Right to Work Poster (English/Spanish)
E-Verify Participation Poster (English/Spanish)
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Axos Bank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Axos Bank AI Hiring
Axos Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.